Introduction to Machine Learning
General Information
| Lecturer: | Prof. Dr. Benno Stein |
| Lab advisors: | Wei-Fan Chen, Yamen Ajjour |
| Workload: | 4.5 ECTS, 2 SWS Lecture, 1 SWS Lab |
| Lecture: | Th. 9:15am - 10:45am (weekly starting October 11th). Marienstraße 13 C - Hörsaal A |
| Lab: | Th. 11:00am - 12:30pm (biweekly starting October 18th). Marienstraße 13 C - Hörsaal C |
| Examination: | 12.2.2019 9:00am - 11:00am Audimax |
Lecturenotes
Labs
- 15.10.2018: Exercise 1 [exercises]
- 18.10.2018: Introduction [slides]
- 25.10.2018: Tutorial [slides, ipython notebook]
- 01.11.2018: Solution of Exercise 1 [slides, ipython notebook]; Tutorial II [slides]
- 01.11.2018: Exercise 2 [exercises]
- 15.11.2018: Solution of Exercise 2
- 15.11.2018: Exercise 3 [exercises]
- 29.11.2018: Solution of Exercise 3 [slides, liveslides, starter code]
- 29.11.2018: Exercise 4 [exercises]
- 06.12.2018: QA
- 13.12.2018: Solution of Exercise 4 [slides, ipython notebook]
- 13.12.2018: Exercise 5 [exercises]
- 20.12.2018: Tutorial [slides]
- 10.01.2019: Solution of Exercise 5 [slides, ipython notebook]
- 10.01.2019: Exercise 6 [exercises]
- 24.01.2019: Solution of Exercise 6 [ipython notebook]
- 24.01.2019: Exercise 7 [exercises] - only for Digital Engineering
- 31.01.2019: Solution of Exercise 7 [ipython notebook]
The deadline and the exercises to be submitted are on the exercise sheet.
Group work up to 3 people is allowed. Please provide both your name(s) and student number(s) with your solutions. To submit your solutions, send an email to Wei-Fan Chen and Yamen Ajjour with a single PDF file or ZIP archive, and name the file <last name>-<student number>-ml-lab<lab-class-number>.[pdf|zip], e.g. "meier-4711-schulz-1234-ml-lab3.pdf" or "meier-4711-schulz-1234-ml-lab3.zip". If you need to submit source code, include it in the ZIP archive. Thank you!
Literature
Machine Learning:
- Christopher M. Bishop. Pattern Recognition and Machine Learning. 2nd edition, Springer 2007.
- Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone. Classification and Regression Trees. CRC Press reprint, 1998.
- Nello Cristianini, John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000.
- Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning. 2nd edition, Springer, 2009.
- Tom Mitchell. Machine Learning. 1st edition, McGraw-Hill, 1997.
- Vladimir Vapnik. The Nature of Statistical Learning Theory. 2nd edition, Springer 2000.
Data Mining:
- David Hand, Heikki Mannila, Padhraic Smyth. Principles of Data Mining. Bradford, 2001.
- Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. 1st edition, Addison Wesley, 2005.
- Ian H. Witten, Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques. 3rd edition, Morgan Kaufmann, 2011.
- Anil K. Jain. Data Clustering: 50 Years Beyond K-Means. Pattern Recognition Letters, Vol. 31, Issue 8, 2010.
Background:
- Eliezer S. Yudkowski. An Intuitive Explanation of Bayes' Theorem. 2003